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Record W2805521597 · doi:10.1038/s41929-018-0084-7

Steering post-C–C coupling selectivity enables high efficiency electroreduction of carbon dioxide to multi-carbon alcohols

2018· article· en· W2805521597 on OpenAlexafffund
Tao‐Tao Zhuang, Zhiqin Liang, Ali Seifitokaldani, Yi Li, Phil De Luna, Thomas Burdyny, Fanglin Che, Fei Meng, Yimeng Min, Rafael Quintero‐Bermudez, Cao‐Thang Dinh, Yuanjie Pang, Miao Zhong, Bo Zhang, Jun Li, Peining Chen, X. R. Zheng, Hongyan Liang, Wenna Ge, Bangjiao Ye, David Sinton, Shu‐Hong Yu, Edward H. Sargent

Bibliographic record

VenueNature Catalysis · 2018
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilMitacsUniversity of TorontoOntario Centres of ExcellenceFonds de recherche du Québec – Nature et technologiesCanadian Light SourceFoundation for Innovative Research Groups of the National Natural Science Foundation of ChinaNational Natural Science Foundation of ChinaCanadian Institute for Advanced Research
KeywordsCatalysisSelectivityCopperElectrochemistryEthyleneChemistryNanoparticleFaraday efficiencyElectrochemical reduction of carbon dioxideCarbon fibersAlcoholMaterials scienceChemical engineeringInorganic chemistryCarbon monoxideNanotechnologyOrganic chemistryElectrodeComposite materialPhysical chemistry

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.251
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations777
Published2018
Admission routes2
Has abstractno

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